building-threat-hunt-hypothesis-framework skill (Anthropic-Cybersecurity-Skills)

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What it does. Build a systematic threat-hunt workflow that turns threat intelligence and ATT&CK gap analysis into testable hypotheses, then executes and validates them via EDR/SIEM queries (CrowdStrike, Defender, Splunk, Elastic, Sysmon, Velociraptor, Sigma) and documents findings in a standardized hunt report. Use when planning or running a proactive threat hunt or scoping compromise from an intel- or anomaly-driven lead. Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/building-threat-hunt-hypothesis-framework/SKILL.md
License Apache-2.0 (skill folder LICENSE)
Author mukul975
Fetched 2026-09-10

Install

  • npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-threat-hunt-hypothesis-framework, or copy the skill folder into ~/.claude/skills/building-threat-hunt-hypothesis-framework/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/building-threat-hunt-hypothesis-framework/SKILL.md

SKILL.md (verbatim)

name: building-threat-hunt-hypothesis-framework
description: Build a systematic threat-hunt workflow that turns threat intelligence and ATT&CK gap analysis into testable hypotheses, then executes and validates them via EDR/SIEM queries (CrowdStrike, Defender, Splunk, Elastic, Sysmon, Velociraptor, Sigma) and documents findings in a standardized hunt report. Use when planning or running a proactive threat hunt or scoping compromise from an intel- or anomaly-driven lead.
domain: cybersecurity
subdomain: threat-hunting
tags:
- threat-hunting
- methodology
- hypothesis
- threat-intelligence
- hunting-framework
- proactive-detection
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1071
- T1059.001
- T1055
- T1547

Building Threat Hunt Hypothesis Framework

When to Use

  • When proactively hunting for indicators of building threat hunt hypothesis framework in the environment
  • After threat intelligence indicates active campaigns using these techniques
  • During incident response to scope compromise related to these techniques
  • When EDR or SIEM alerts trigger on related indicators
  • During periodic security assessments and purple team exercises

Prerequisites

  • EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
  • SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
  • Sysmon deployed with comprehensive configuration
  • Windows Security Event Log forwarding enabled
  • Threat intelligence feeds for IOC correlation

Workflow

  1. Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
  2. Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
  3. Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
  4. Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
  5. Validate Findings: Distinguish true positives from false positives through contextual analysis.
  6. Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
  7. Document and Report: Record findings, update detection rules, and recommend response actions.

Key Concepts

Concept Description
TA0001 Initial Access
TA0003 Persistence
TA0008 Lateral Movement
TA0010 Exfiltration

Tools & Systems

Tool Purpose
CrowdStrike Falcon EDR telemetry and threat detection
Microsoft Defender for Endpoint Advanced hunting with KQL
Splunk Enterprise SIEM log analysis with SPL queries
Elastic Security Detection rules and investigation timeline
Sysmon Detailed Windows event monitoring
Velociraptor Endpoint artifact collection and hunting
Sigma Rules Cross-platform detection rule format

Common Scenarios

  1. Scenario 1: Intelligence-driven hunt based on APT campaign report
  2. Scenario 2: ATT&CK coverage gap analysis driving hypothesis creation
  3. Scenario 3: Anomaly-driven hypothesis from UEBA alert investigation
  4. Scenario 4: Situational awareness hunt based on industry sector threats

Output Format

Hunt ID: TH-BUILDI-[DATE]-[SEQ]
Technique: TA0001
Host: [Hostname]
User: [Account context]
Evidence: [Log entries, process trees, network data]
Risk Level: [Critical/High/Medium/Low]
Confidence: [High/Medium/Low]
Recommended Action: [Containment, investigation, monitoring]

Other files in this skill

assets/template.md (verbatim)

Building Threat Hunt Hypothesis Framework - Hunt Template

Hunt Metadata

Field Value
Hunt ID TH-BUILDI-YYYY-MM-DD-NNN
Analyst
Date Started
Date Completed
Status [ ] In Progress / [ ] Complete
Priority [ ] Critical / [ ] High / [ ] Medium / [ ] Low

Hypothesis

Statement: [Formulate a clear, testable hypothesis]

Basis: [ ] Threat Intel / [ ] ATT&CK Gap / [ ] Anomaly / [ ] Incident Follow-up

Target Techniques

  • TA0001 - Initial Access
  • TA0003 - Persistence
  • TA0008 - Lateral Movement
  • TA0010 - Exfiltration

Data Sources

  • Sysmon Event Logs
  • Windows Security Event Logs
  • EDR Telemetry (Platform: _____________)
  • SIEM (Platform: _____________)
  • Network Logs (Proxy/Firewall/DNS)
  • Cloud Audit Logs
  • Email Gateway Logs
  • Application Logs

Queries Executed

Query 1: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Query 2: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Findings

# Timestamp Host User Technique Evidence Summary Risk Verdict
1 TP / FP / BTP
2 TP / FP / BTP
3 TP / FP / BTP

IOCs Discovered

Network IOCs

Type Value Context Confidence
IP
Domain
URL

Host IOCs

Type Value Context Confidence
SHA256
Filename
Registry Key
Scheduled Task

Hunt Results Summary

Metric Count
Total Events Analyzed
Anomalies Identified
True Positives
False Positives
Benign True Positives
New IOCs Discovered
Detection Rules Created
Detection Rules Updated

Hypothesis Outcome

  • Confirmed: Evidence supports the hypothesis
  • Partially Confirmed: Some evidence found, further investigation needed
  • Refuted: No evidence found
  • Inconclusive: Insufficient data

Recommendations

  1. Immediate Actions: [Containment, remediation steps]
  2. Detection Improvements: [New rules, tuning recommendations]
  3. Visibility Gaps: [Missing data sources, coverage needs]
  4. Security Hardening: [Configuration changes, policy updates]
  5. Follow-up Hunts: [Related hypotheses to investigate]

Analyst Notes

[Free-form notes, observations, and lessons learned]

references/api-reference.md (verbatim)

API Reference: Threat Hunt Hypothesis Framework

Hypothesis Structure

Field Description
hypothesis_id Unique identifier (HYP-XXXXXXXX)
technique_id MITRE ATT&CK technique (e.g. T1059.001)
hypothesis_statement Natural language hypothesis
data_sources Required log sources
priority high / medium / low
status planned / in_progress / completed

MITRE ATT&CK Data Sources

# Download ATT&CK STIX bundle
curl -O https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json

# Filter attack-pattern objects for technique data sources
python3 -c "
import json
bundle = json.load(open('enterprise-attack.json'))
for obj in bundle['objects']:
    if obj.get('type') == 'attack-pattern' and not obj.get('x_mitre_deprecated'):
        eid = obj['external_references'][0]['external_id']
        ds = [d['source_name'] for d in obj.get('x_mitre_data_sources', [])]
        print(f'{eid}: {ds}')
"

Hunt Maturity Model (HMM)

Level Name Description
HM0 Initial Ad hoc, no documented procedures
HM1 Minimal Basic procedures, limited data sources
HM2 Procedural Documented hypotheses, repeatable hunts
HM3 Innovative Custom analytics, TI-driven hypotheses
HM4 Leading Automated, ML-assisted, continuous hunting

Key Windows Event IDs for Hunting

Event ID Source Use Case
4104 PowerShell Script block logging
4688 Security Process creation
4624/4625 Security Logon success/failure
4698 Security Scheduled task created
1 (Sysmon) Sysmon Process create with hashes
3 (Sysmon) Sysmon Network connection
10 (Sysmon) Sysmon Process access (LSASS)
11 (Sysmon) Sysmon File create

Sigma Rule Integration

title: Suspicious PowerShell Execution
status: experimental
logsource:
    product: windows
    service: powershell
detection:
    selection:
        EventID: 4104
        ScriptBlockText|contains:
            - 'Invoke-Mimikatz'
            - 'Invoke-Expression'
    condition: selection
level: high

references/standards.md (verbatim)

Standards and References - Building Threat Hunt Hypothesis Framework

MITRE ATT&CK Mappings

Technique Name Description
TA0001 Initial Access See attack.mitre.org/techniques/TA0001
TA0003 Persistence See attack.mitre.org/techniques/TA0003
TA0008 Lateral Movement See attack.mitre.org/techniques/TA0008
TA0010 Exfiltration See attack.mitre.org/techniques/TA0010

Detection Data Sources

Source Event ID Purpose
Sysmon 1 Process creation with command line
Sysmon 3 Network connection initiated
Sysmon 7 Image loaded (DLL)
Sysmon 10 Process access (LSASS)
Sysmon 11 File creation
Sysmon 12/13 Registry create/set
Sysmon 22 DNS query
Sysmon 25 Process tampering
Windows Security 4624 Successful logon
Windows Security 4625 Failed logon
Windows Security 4648 Explicit credential logon
Windows Security 4672 Special privileges assigned
Windows Security 4688 Process creation
Windows Security 4697 Service installed
Windows Security 4698 Scheduled task created
Windows Security 4769 Kerberos TGS requested
Windows Security 5140 Network share accessed

References

references/workflows.md (verbatim)

Detailed Hunting Workflow - Building Threat Hunt Hypothesis Framework

Phase 1: Data Collection and Querying

Splunk SPL Query

| makeresults
| eval hypothesis="Adversaries may be using [TECHNIQUE] to [OBJECTIVE] against [TARGET] via [VECTOR]"
| eval data_sources="[List required data sources]"
| eval queries="[Specific SPL queries to test hypothesis]"
| eval success_criteria="[What constitutes confirming/refuting hypothesis]"

KQL Query (Microsoft Defender for Endpoint)

let HuntHypothesis = datatable(Component:string, Description:string)
[
    "Technique", "MITRE ATT&CK technique being hunted",
    "Target", "Systems or accounts in scope",
    "Data Sources", "Logs and telemetry required",
    "Indicators", "Observable evidence of technique",
    "Success Criteria", "What confirms or refutes hypothesis"
];
HuntHypothesis

Phase 2: Baseline and Anomaly Detection

Step 2.1 - Establish Normal Behavior Baseline

  • Collect 30 days of historical data for the targeted technique
  • Document expected patterns, frequencies, and legitimate use cases
  • Identify known false positive sources and document exceptions
  • Build statistical baseline (mean, standard deviation) for key metrics

Step 2.2 - Identify Anomalies

  • Compare current activity against the 30-day baseline
  • Flag events exceeding 3 standard deviations from normal
  • Prioritize anomalies by risk score and potential business impact
  • Cross-reference with threat intelligence for known IOCs

Phase 3: Investigation and Correlation

Step 3.1 - Deep Dive Analysis

  • For each anomaly, collect full process tree context
  • Correlate with network activity, file operations, and authentication events
  • Check binary signatures, file hashes, and certificate validity
  • Review user account context and access patterns

Step 3.2 - Attack Chain Reconstruction

  • Map findings to MITRE ATT&CK kill chain stages
  • Identify initial access vector if applicable
  • Trace lateral movement and privilege escalation paths
  • Determine data access and potential exfiltration

Phase 4: Validation and Response

Step 4.1 - True/False Positive Determination

  • Verify findings with system owners and IT operations
  • Check change management records for authorized activities
  • Validate user context (authorized actions vs. compromised account)
  • Document determination rationale for each finding

Step 4.2 - Response Actions

  • For confirmed threats: initiate incident response procedures
  • For detection gaps: create or update detection rules
  • For false positives: tune existing rules and update exclusions
  • Update threat hunting playbook with lessons learned

Phase 5: Documentation and Reporting

Step 5.1 - Hunt Report

  • Summarize hypothesis, methodology, and findings
  • Include all queries executed and their results
  • Document IOCs discovered and detection rules created
  • Provide recommendations for security improvements

Step 5.2 - Knowledge Base Update

  • Add findings to threat intelligence platform
  • Update MITRE ATT&CK coverage heatmap
  • Share detection rules via Sigma format
  • Schedule follow-up hunts for related techniques

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